Hippocampal place cell sequences depict future paths to remembered goals

By Brad E. Pfeiffer, David J. Foster. 2013

1,151 citations recorded by the source corpus.

Effective navigation requires planning extended routes to remembered goal locations. Hippocampal place cells have been proposed to have a role in navigational planning, but direct evidence has been lacking. Here we show that before goal-directed navigation in an open arena, the rat hippocampus generates brief sequences encoding spatial trajectories strongly biased to progress from the subject’s current location to a known goal location. These sequences predict immediate future behaviour, even in cases in which the specific combination of start and goal locations is novel. These results indicate that hippocampal sequence events characterized previously in linearly constrained environments as ‘replay’ are also capable of supporting a goal-directed, trajectory-finding mechanism, which identifies important places and relevant behavioural paths, at specific times when memory retrieval is required, and in a manner that could be used to control subsequent navigational behaviour.

Subfields: Cognitive Neuroscience, Developmental and Educational Psychology

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Citations in the corpus

Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021, neutral): “Experimental and theoretical studies have identified partial replay as a potential mechanism for exploring possible routes or facilitating goal-directed navigation (Foster & Knierim, 2012; Pfeiffer & Foster, 2013).”
Model-Based and Model-Free Pavlovian Reward Learning: Revaluation, Revision and Revelation (2014, neutral): “…and the default network might be involved in model-based prediction and control (Hassabis, Kumaran, Vann, & Maguire, 2007; Johnson & Redish, 2007; Pfeiffer & Foster, 2013; Schacter, Addis, & Buckner, 2008; Schacter et al., 2012; Spreng, Mar, & Kim, 2009; van der Meer, Johnson, Schmitzer-Torbert,…”
A dedicated population for reward coding in the hippocampus (2018, neutral): “For example, animals might explore possible routes in advance of movement (Pfeiffer and Foster, 2013; Johnson and Redish, 2007), in which case activating reward-associated cells would indicate a successful route.”
What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated. (2016, positive): “…shortcut paths by stitching together components of trajectories [72,73]; (iii) support look-ahead online planning during goal-directed behavior [70,74]; (iv) re ﬂ ect trajectories through parts of environments that have only been seen but never visited [75]; and (v) be biased to re ﬂ ect…”
On the methods for reactivation and replay analysis (2020, background): “Pfeiffer BE, Foster DJ. 2015 Autoassociative dynamics in the generation of sequences of hippocampal place cells.”
Hippocampal Reactivation of Random Trajectories Resembling Brownian Diffusion. (2019, background): “So far, only one study has been able to see the reactivation of 2D trajectories (Pfeiffer and Foster, 2013), in that case during awake, exploration-associated SWRs.”
Hippocampal replays under the scrutiny of reinforcement learning models. (2018, methodology): “…periods, when the animal is consuming a food reward (Gupta et al. 2010), when it is waiting at the start of the maze during an intertrial interval (Diba and Buzsáki 2007), or when it is preparing a movement toward its starting point or “home” location within the maze (Pfeiffer and Foster 2013).”
Integrating memories to guide decisions (2015, background): “This can be seen at choice points [20], can predict the direction of the animal’s own subsequent locomotion [21], and can be integrative in the sense of knitting together ‘‘subroutes’’ that have not actually yet been experienced sequentially [22].”